Echo State Network prediction based on Backtracking Search optimization Algorithm

Wu, Shihong; Wang, Zhigang; Ling, Darong

doi:10.1109/itnec.2019.8729414

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…An ESN-based predictive approach has been pursued in paper [28], in which mobile communication traffic has been forecasted considering complex network structures such as small world topologies. Then, an ESN has been also adopted by the authors of [29], contextualizing its application in time series predictions and involving a binary particle swarm algorithm to determine the ESN matrix weight connections. Furthermore, in paper [30], the particle swarm strategy has been applied to design both a single-and the multi-objective optimizer structure.…”

Section: Related Workmentioning

confidence: 99%

“…[18] Proposal of a prediction approach of the time when a given user has to stop the game [19] Game server approach [20,21] Payment solutions [22][23][24][25]29] Collaborative learning process [23][24][25]29] User trajectory prediction [26] Proactive handover approach [27] Dynamic human blockage prediction [30] Multi-objective optimization [31] Regularization method [32] ESN-based power supply prediction [33] Optimal congestion control strategies Finally, in comparison to Table 1 (the existing literature), the main contributions of the paper can be summarized as follows:…”

Section: Related Workmentioning

confidence: 99%

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Echo State Learning for User Trajectory Prediction to Minimize Online Game Breaks in 6G Terahertz Networks

Picano

Scommegna

Vicario

et al. 2023

JSAN

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Mobile online gaming is constantly growing in popularity and is expected to be one of the most important applications of upcoming sixth generation networks. Nevertheless, it remains challenging for game providers to support it, mainly due to its intrinsic and ever-stricter need for service continuity in the presence of user mobility. In this regard, this paper proposes a machine learning strategy to forecast user channel conditions, aiming at guaranteeing a seamless service whenever a user is involved in a handover, i.e., moving from the coverage area of one base station towards another. In particular, the proposed channel condition prediction approach involves the exploitation of an echo state network, an efficient class of recurrent neural network, that is empowered with a genetic algorithm to perform parameter optimization. The echo state network is applied to improve user decisions regarding the selection of the serving base station, avoiding game breaks as much as possible to lower game lag time. The validity of the proposed framework is confirmed by simulations in comparison to the long short-term memory approach and another alternative method, aimed at thoroughly testing the accuracy of the learning module in forecasting user trajectories and in reducing game breaks or lag time, with a focus on a sixth generation network application scenario.

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Section: Related Workmentioning

confidence: 99%